from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

daal4py_LogisticRegression: 0h 0m 4s

daal4py_KMeans_tall: 0h 0m 9s

Ridge: 0h 0m 10s

LogisticRegression: 0h 0m 23s

KMeans_tall: 0h 0m 23s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 30s

KNeighborsClassifier_kd_tree: 0h 2m 33s

HistGradientBoostingClassifier: 0h 5m 3s

lightgbm: 0h 5m 3s

daal4py_KNeighborsClassifier: 0h 5m 8s

catboost_symmetric: 0h 5m 27s

catboost_lossguide: 0h 5m 39s

xgboost: 0h 5m 45s

KNeighborsClassifier: 0h 33m 4s

total: 1h 9m 35s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.301	0.000	2.661	0.000	-1	5	NaN	NaN	0.449	0.000	0.669	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	34.948	0.000	0.000	0.035	-1	5	0.825	0.941	3.744	0.036	9.335	0.089	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.188	0.011	0.000	0.188	-1	5	1.000	1.000	0.096	0.002	1.956	0.122	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.118	0.000	6.772	0.000	1	5	NaN	NaN	0.436	0.000	0.271	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	21.937	0.291	0.000	0.022	1	5	0.825	0.712	3.653	0.020	6.005	0.086	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.187	0.004	0.000	0.187	1	5	1.000	1.000	0.097	0.002	1.918	0.058	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.119	0.000	6.718	0.000	-1	1	NaN	NaN	0.439	0.000	0.271	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	26.950	0.170	0.000	0.027	-1	1	0.698	0.806	3.647	0.017	7.391	0.058	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.187	0.014	0.000	0.187	-1	1	1.000	1.000	0.096	0.001	1.949	0.151	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.116	0.000	6.904	0.000	-1	100	NaN	NaN	0.441	0.000	0.263	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	33.995	0.000	0.000	0.034	-1	100	0.927	0.806	3.659	0.030	9.290	0.076	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.184	0.009	0.000	0.184	-1	100	1.000	1.000	0.097	0.002	1.888	0.096	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.114	0.000	6.991	0.000	1	100	NaN	NaN	0.436	0.000	0.262	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	21.561	0.098	0.000	0.022	1	100	0.927	0.941	3.735	0.032	5.773	0.055	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.185	0.004	0.000	0.185	1	100	1.000	1.000	0.097	0.002	1.905	0.051	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.113	0.000	7.081	0.000	1	1	NaN	NaN	0.444	0.000	0.254	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	14.343	0.059	0.000	0.014	1	1	0.698	0.712	3.635	0.024	3.945	0.031	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.180	0.002	0.000	0.180	1	1	1.000	1.000	0.099	0.007	1.809	0.135	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.051	0.000	0.312	0.000	-1	5	NaN	NaN	0.095	0.000	0.539	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.141	0.000	0.000	0.031	-1	5	0.983	0.982	0.840	0.010	37.069	0.456	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.002	0.000	0.022	-1	5	1.000	1.000	0.004	0.000	5.003	0.573	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.302	0.000	1	5	NaN	NaN	0.091	0.000	0.582	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	17.267	0.093	0.000	0.017	1	5	0.983	0.975	0.777	0.011	22.215	0.348	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.016	0.001	0.000	0.016	1	5	1.000	1.000	0.004	0.000	4.118	0.429	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.310	0.000	-1	1	NaN	NaN	0.093	0.000	0.553	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.957	0.108	0.000	0.024	-1	1	0.970	0.981	0.773	0.008	31.004	0.338	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.019	0.002	0.000	0.019	-1	1	1.000	1.000	0.004	0.000	4.848	0.601	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.277	0.000	-1	100	NaN	NaN	0.091	0.000	0.635	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.019	0.000	0.000	0.031	-1	100	0.984	0.981	0.775	0.014	40.005	0.738	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.001	0.000	0.021	-1	100	1.000	1.000	0.004	0.000	5.237	0.540	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.059	0.000	0.273	0.000	1	100	NaN	NaN	0.094	0.000	0.625	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	17.315	0.090	0.000	0.017	1	100	0.984	0.982	0.851	0.016	20.341	0.405	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.016	0.001	0.000	0.016	1	100	1.000	1.000	0.004	0.000	3.782	0.287	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.310	0.000	1	1	NaN	NaN	0.094	0.000	0.546	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	9.895	0.057	0.000	0.010	1	1	0.970	0.975	0.784	0.016	12.625	0.266	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.014	0.001	0.000	0.014	1	1	1.000	1.000	0.004	0.000	3.107	0.279	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.849	0.000	0.028	0.000	1	100	NaN	NaN	0.709	0.000	4.020	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.698	0.022	0.000	0.005	1	100	0.975	0.962	0.109	0.001	43.245	0.496	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.000	0.000	8.473	4.534	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.959	0.000	0.027	0.000	1	1	NaN	NaN	0.696	0.000	4.252	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.749	0.021	0.000	0.001	1	1	0.959	0.982	0.617	0.014	1.213	0.043	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.001	0.000	1.461	0.723	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.892	0.000	0.028	0.000	-1	100	NaN	NaN	0.699	0.000	4.140	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.571	0.029	0.000	0.003	-1	100	0.975	0.981	0.207	0.003	12.444	0.248	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.000	0.000	13.685	5.917	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.818	0.000	0.028	0.000	-1	5	NaN	NaN	0.708	0.000	3.979	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.786	0.017	0.000	0.001	-1	5	0.978	0.982	0.620	0.010	1.268	0.035	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.001	0.000	4.365	1.602	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.826	0.000	0.028	0.000	-1	1	NaN	NaN	0.727	0.000	3.887	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.416	0.011	0.000	0.000	-1	1	0.959	0.962	0.114	0.002	3.645	0.121	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	9.133	3.539	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.808	0.000	0.028	0.000	1	5	NaN	NaN	0.727	0.000	3.861	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.392	0.018	0.000	0.001	1	5	0.978	0.981	0.208	0.015	6.683	0.488	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	3.715	2.383	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.719	0.000	0.022	0.000	1	100	NaN	NaN	0.456	0.000	1.576	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.059	0.003	0.000	0.000	1	100	0.977	0.978	0.001	0.000	76.523	21.076	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	4.637	1.999	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.720	0.000	0.022	0.000	1	1	NaN	NaN	0.486	0.000	1.480	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.034	0.001	0.000	0.000	1	1	0.968	0.990	0.007	0.001	4.950	0.421	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.361	1.960	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.726	0.000	0.022	0.000	-1	100	NaN	NaN	0.470	0.000	1.544	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.055	0.003	0.000	0.000	-1	100	0.977	0.989	0.001	0.000	50.497	12.540	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	14.778	7.226	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.743	0.000	0.022	0.000	-1	5	NaN	NaN	0.467	0.000	1.591	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.039	0.003	0.000	0.000	-1	5	0.976	0.990	0.007	0.001	5.747	0.680	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	14.482	6.895	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.727	0.000	0.022	0.000	-1	1	NaN	NaN	0.474	0.000	1.534	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.037	0.002	0.000	0.000	-1	1	0.968	0.978	0.001	0.000	45.209	11.751	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	14.831	7.357	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.723	0.000	0.022	0.000	1	5	NaN	NaN	0.485	0.000	1.489	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.037	0.002	0.000	0.000	1	5	0.976	0.989	0.001	0.000	34.065	8.462	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	6.245	3.548	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.677	0.000	0.709	0.000	k-means++	NaN	30	NaN	0.285	0.0	2.375	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.321	0.000	k-means++	0.001	30	0.001	0.000	0.0	6.507	2.199	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.001	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	7.034	3.352	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.498	0.000	0.964	0.000	random	NaN	30	NaN	0.303	0.0	1.645	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.298	0.000	random	0.001	30	0.000	0.000	0.0	6.659	2.832	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	random	1.000	30	1.000	0.000	0.0	5.279	1.586	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	7.073	0.000	3.393	0.000	k-means++	NaN	30	NaN	3.494	0.0	2.025	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	11.164	0.000	k-means++	0.002	30	0.001	0.000	0.0	6.824	3.593	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.017	0.001	k-means++	1.000	30	1.000	0.000	0.0	7.517	3.345	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.295	0.000	3.812	0.000	random	NaN	30	NaN	3.661	0.0	1.719	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	13.300	0.000	random	0.002	30	0.002	0.000	0.0	4.822	2.007	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.016	0.002	random	1.000	30	1.000	0.000	0.0	6.737	2.921	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.089	0.000	0.036	0.000	random	NaN	20	NaN	0.046	0.0	1.944	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.001	0.148	0.000	random	-0.001	20	0.002	0.001	0.0	3.460	0.987	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.000	0.000	0.002	random	1.000	20	1.000	0.000	0.0	8.587	3.570	See	See
3	KMeans_short	fit	10000	10000	2	20	0.301	0.000	0.011	0.000	k-means++	NaN	20	NaN	0.119	0.0	2.532	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.001	0.152	0.000	k-means++	-0.002	20	0.001	0.001	0.0	3.042	0.906	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.000	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.029	4.255	See	See
6	KMeans_short	fit	10000	10000	100	20	0.303	0.000	0.528	0.000	random	NaN	20	NaN	0.231	0.0	1.311	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.001	4.905	0.000	random	0.272	20	0.266	0.001	0.0	2.286	0.495	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.009	0.002	random	1.000	20	1.000	0.000	0.0	7.784	3.213	See	See
9	KMeans_short	fit	10000	10000	100	20	1.037	0.000	0.154	0.000	k-means++	NaN	20	NaN	0.582	0.0	1.780	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.539	0.000	k-means++	0.282	20	0.234	0.001	0.0	2.063	0.252	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	6.745	3.325	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	14.567	0.000	[-0.08099738]	0.000	NaN	NaN	NaN	NaN	NaN	2.633	0.0	5.532	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.000	[43.13082332]	0.000	NaN	NaN	NaN	NaN	0.522	0.000	0.0	0.870	0.314	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.000	[0.14028762]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.383	0.253	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	1.195	0.000	[-1.78615678]	0.001	NaN	NaN	NaN	NaN	NaN	0.864	0.0	1.384	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.001	[89.37479723]	0.000	NaN	NaN	NaN	NaN	0.280	0.004	0.0	0.598	0.148	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.000	[12.01989292]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.180	0.113	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.270	0.0	0.296	0.0	NaN	NaN	NaN	0.265	0.0	1.019	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	7.802	0.0	NaN	NaN	0.09	0.017	0.0	0.614	0.020	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	0.742	0.0	NaN	NaN	NaN	0.000	0.0	0.697	0.420	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.383	0.0	0.579	0.0	NaN	NaN	NaN	0.345	0.0	4.008	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	3.973	0.0	NaN	NaN	1.00	0.000	0.0	0.796	0.399	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.009	0.0	NaN	NaN	NaN	0.000	0.0	0.634	0.470	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw results

Smoothed HPO Curves

Speedup barplots